knitr document van Steensel lab
TF reporter barcode processing - pMT02 - stimulation 1
Introduction
18,000 TF reporters on pMT02 were transfected into mESCs and NPCs (in total 7 different conditions), sequencing data yielded barcode counts of these experiments. These counts will be processed in this script.
Analysis
Compare bartender with starcode
Conlusion barcode clustering:
- optimal starcode settings: levenshtein distance of 1
- optimal bartender settings: hamming distance of 2
- starcode and bartender perform equally well - starcode a bit better
- next time design barcodes requiring levenshtein distance of 3
Add barcode annotation to barcode counts & extract first bc read count information
Get a closer look at unmatched barcodes
Check if the pDNA-bc count correlates with the barcode count in the pDNA-insert-seq data
Conclusion barcode clustering:
- I manually added barcodes with high correlation and levenshtein distance of 1 to 1 barcode to get more reads
Data quality plots
Normalization of barcode counts:
Divide cDNA barcode counts through pDNA barcode counts to get activity
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## [1] "progress: 100 %"Calculate mean activity - filter out outlier barcodes
Calculate correlations between technical replicates
Data quality plots - correlation between replicates
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Session Info
## [1] "Run time: 17.65161 mins"## [1] "/DATA/usr/m.trauernicht/projects/SuRE-TF/gen-1_stimulation-1"## [1] "Thu Nov 26 10:01:03 2020"## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.7 LTS
##
## Matrix products: default
## BLAS: /usr/lib/libblas/libblas.so.3.6.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] LncFinder_1.1.4 gridExtra_2.3 RColorBrewer_1.1-2
## [4] readr_1.3.1 haven_2.2.0 ggbeeswarm_0.6.0
## [7] plotly_4.9.2.1 tibble_3.0.1 dplyr_0.8.5
## [10] vwr_0.3.0 latticeExtra_0.6-29 lattice_0.20-38
## [13] stringdist_0.9.5.5 GGally_1.5.0 ggpubr_0.2.5
## [16] magrittr_1.5 ggplot2_3.3.0 stringr_1.4.0
## [19] plyr_1.8.6 data.table_1.12.8
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-143 lubridate_1.7.4 httr_1.4.1
## [4] tools_3.6.3 R6_2.5.0 rpart_4.1-15
## [7] vipor_0.4.5 lazyeval_0.2.2 mgcv_1.8-31
## [10] colorspace_1.4-1 ade4_1.7-13 nnet_7.3-12
## [13] withr_2.1.2 tidyselect_1.1.0 compiler_3.6.3
## [16] labeling_0.3 scales_1.1.0 digest_0.6.27
## [19] rmarkdown_2.5 jpeg_0.1-8.1 pkgconfig_2.0.3
## [22] htmltools_0.5.0 fastmap_1.0.1 htmlwidgets_1.5.2
## [25] rlang_0.4.8 shiny_1.4.0 prettydoc_0.4.0
## [28] generics_0.0.2 farver_2.0.1 jsonlite_1.7.1
## [31] crosstalk_1.0.0 ModelMetrics_1.2.2.1 Matrix_1.2-18
## [34] Rcpp_1.0.5 munsell_0.5.0 lifecycle_0.2.0
## [37] stringi_1.5.3 pROC_1.16.1 yaml_2.2.1
## [40] MASS_7.3-51.5 recipes_0.1.9 grid_3.6.3
## [43] promises_1.1.1 forcats_0.4.0 crayon_1.3.4
## [46] splines_3.6.3 hms_0.5.3 knitr_1.30
## [49] pillar_1.4.3 seqinr_3.6-1 ggsignif_0.6.0
## [52] reshape2_1.4.4 codetools_0.2-16 stats4_3.6.3
## [55] glue_1.4.2 evaluate_0.14 httpuv_1.5.4
## [58] png_0.1-7 vctrs_0.2.4 foreach_1.4.7
## [61] gtable_0.3.0 purrr_0.3.3 tidyr_1.0.0
## [64] reshape_0.8.8 assertthat_0.2.1 xfun_0.19
## [67] gower_0.2.1 mime_0.9 prodlim_2019.11.13
## [70] xtable_1.8-4 e1071_1.7-4 later_1.1.0.1
## [73] class_7.3-15 survival_3.1-8 viridisLite_0.3.0
## [76] timeDate_3043.102 iterators_1.0.12 beeswarm_0.2.3
## [79] lava_1.6.6 ellipsis_0.3.0 caret_6.0-85
## [82] ipred_0.9-9